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⛄ 内容介绍
针对麻雀搜索算法收敛速度慢、难以跳出局部最优等问题,提出一种基于t分布变异的改进麻雀搜索算法.在更新麻雀种群加入者位置后,引入自适应t分布变异,对加入者位置产生扰动,避免陷入局部最优,增强算法性能;通过比较灰狼优化算法、飞蛾火焰优化算法和原始麻雀搜索算法,在6个基准函数上进行仿真实验.实验结果和Wilcoxon符号秩检验结果都表明所提出的改进麻雀搜索算法的收敛精度与速度均优于其他算法,达到提高算法收敛速度,增强算法跳出局部极值能力的效果.
⛄ 部分代码
%_________________________________________________________________________%
% 麻雀优化算法 %
%_________________________________________________________________________%
function [Best_pos,Best_score,curve]=SSA(pop,Max_iter,lb,ub,dim,fobj)
ST = 0.6;%预警值
PD = 0.7;%发现者的比列，剩下的是加入者
SD = 0.2;%意识到有危险麻雀的比重
PDNumber = pop*PD; %发现者数量
SDNumber = pop - pop*PD;%意识到有危险麻雀数量
if(max(size(ub)) == 1)
ub = ub.*ones(1,dim);
lb = lb.*ones(1,dim);
end
%种群初始化
X0=initialization(pop,dim,ub,lb);
X = X0;
%计算初始适应度值
fitness = zeros(1,pop);
for i = 1:pop
fitness(i) = fobj(X(i,:));
end
[fitness, index]= sort(fitness);%排序
BestF = fitness(1);
WorstF = fitness(end);
GBestF = fitness(1);%全局最优适应度值
for i = 1:pop
X(i,:) = X0(index(i),:);
end
curve=zeros(1,Max_iter);
GBestX = X(1,:);%全局最优位置
X_new = X;
for i = 1: Max_iter
BestF = fitness(1);
WorstF = fitness(end);
R2 = rand(1);
for j = 1:PDNumber
if(R2<ST)
X_new(j,:) = X(j,:).*exp(-j/(rand(1)*Max_iter));
else
X_new(j,:) = X(j,:) + randn()*ones(1,dim);
end
end
for j = PDNumber+1:pop
% if(j>(pop/2))
if(j>(pop - PDNumber)/2 + PDNumber)
X_new(j,:)= randn().*exp((X(end,:) - X(j,:))/j^2);
else
%产生-1，1的随机数
A = ones(1,dim);
for a = 1:dim
if(rand()>0.5)
A(a) = -1;
end
end
AA = A'*inv(A*A');
X_new(j,:)= X(1,:) + abs(X(j,:) - X(1,:)).*AA';
end
end
Temp = randperm(pop);
SDchooseIndex = Temp(1:SDNumber);
for j = 1:SDNumber
if(fitness(SDchooseIndex(j))>BestF)
X_new(SDchooseIndex(j),:) = X(1,:) + randn().*abs(X(SDchooseIndex(j),:) - X(1,:));
elseif(fitness(SDchooseIndex(j))== BestF)
K = 2*rand() -1;
X_new(SDchooseIndex(j),:) = X(SDchooseIndex(j),:) + K.*(abs( X(SDchooseIndex(j),:) - X(end,:))./(fitness(SDchooseIndex(j)) - fitness(end) + 10^-8));
end
end
%边界控制
for j = 1:pop
for a = 1: dim
if(X_new(j,a)>ub)
X_new(j,a) =ub(a);
end
if(X_new(j,a)<lb)
X_new(j,a) =lb(a);
end
end
end
%更新位置
for j=1:pop
fitness_new(j) = fobj(X_new(j,:));
end
for j = 1:pop
if(fitness_new(j) < GBestF)
GBestF = fitness_new(j);
GBestX = X_new(j,:);
end
end
X = X_new;
fitness = fitness_new;
%排序更新
[fitness, index]= sort(fitness);%排序
BestF = fitness(1);
WorstF = fitness(end);
for j = 1:pop
X(j,:) = X(index(j),:);
end
curve(i) = GBestF;
end
Best_pos =GBestX;
Best_score = curve(end);
end
⛄ 运行结果
⛄ 参考文献
[1]吴超略, 韦文山, 郭羿,等. 基于t分布变异的改进麻雀搜索算法[J]. 微型机与应用, 2022(008):041.
